- Shweta Jindal, Satya S Bulusu,"A transferable artificial neural network model for atomic forces in nanoparticles",
**J. Chem. Phys.**149 (19), 194101 (2018) - Shweta Jindal, Satya S Bulusu,"An algorithm to use higher order invariants for modelling potential energy surface of nanoclusters",
**Chem. Phys. Lett.**693, 152-158 (2018) - Siva Chiriki, Shweta Jindal, Priya Singh, Satya S Bulusu,"Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster",
**J. Chem. Phys.**149 (7), 074307 (2018) - Shweta Jindal, Siva Chiriki and Satya S. Bulusu, "Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster",
**J. Chem. Phys.**146 (20), 204301 (2017) - Siva Chiriki, Shweta Jindal and Satya S. Bulusu, "Neural network potentials for dynamics and thermodynamics of gold nanoparticles",
**J. Chem. Phys.**146 (8), 084314 (2017) - Siva Chiriki and Satya S. Bulusu, "Modeling of DFT quality neural network potential for sodium clusters: Application to melting of sodium clusters (Na
_{20}to Na_{40})",**Chem. Phys. Lett.**652, 130-135 (2016) - Siva Chiriki, Shweta Jindal and Satya S. Bulusu, "c-T Phase Diagram and Landau Free Energies of (AgAu)
_{55}Nanoalloy Via Neural-Network Molecular Dynamic Simulations",**J. Chem. Phys.**147 (15), 154303 (2017) - Siva Chiriki, Anuradha Dagar, and Satya S. Bulusu, "Structural evolution of nucleobase clusters using force field models and density functional theory",
**Chem. Phys. Lett.**634, 166-173 (2015). - NS Khetrapal, Satya S. Bulusu and XC Zeng, "Structural Evolution of Gold Clusters Au n–(n= 21–25) Revisited",
**J. Phy. Chem. A**121 (12), 2466-2474 (2017) - S Kazachenko, Satya S. Bulusu, AJ Thakkar, "Methanol clusters (CH3OH) n: Putative global minimum-energy structures from model potentials and dispersion-corrected density functional theory",
**J. Chem. Phys.**138 (22), 224303 (2013). - René Fournier and Satya S. Bulusu, "Closed-Shell Metal Clusters" in
**Metal Clusters and Nanoalloys**, (2013), pp 81-103, Springer NY. - A complete list of publications can be found in following link.

**Transferable interatomic potentials for nanoparticles**

**The proposed strategy has definitely made the mapping and fitting of atomic forces easier and can be applied to a wide variety of molecular systems.**

**Bispectrum: Third order invariant for gold nanoparticles**

**We have integrated bispectrum features with artificial neural network (ANN) learning technique in this work. We have also devised an algorithm for selecting the frequencies that need to be coupled for extracting the phase information between different frequency bands.**

**Structure and properties of Au-SH nanoclusters**

**We modeled artificial neural network (ANN) potentials for Aun(SH)m nanoclusters in the range of n = 10 to n = 38.The UV-visible spectral analysis reveals that significant spectroscopic variations are observed at different SH compositions. This study provides a fundamental understanding of structural changes with decreasing SH compositions and with increasing the size of the nanocluster.**

**Integration of spherical harmonics descriptor with neural network**

**In order to decrease the computational cost for calculations of energy and forces for nanoparticles (>100 atoms), we have developed spherical harmonics based descriptor which is applied to neural network. This integrated technique reduces the complexity in molecular dynamics simulations for long time scales.**

**Neural network potential for metallic nanoclusters**

**We have modelled a global potential energy surface using neural network for sodium clusters (Na _{20} to Na_{40}) and gold clusters (Au_{17} to Au_{58}). We have applied these potentials to study the thermal stability, fluxionality, and probabilities along with many other thermodynamic properties.**

**Neural network potential for nano-alloys**

**To examine the effect of composition for gold nanoclusters, we have modelled a global potential energy surface for (AgAu) _{55} system. By applying this potential we have derived c-T Phase Diagram and Landau Free Energies to check the thermal stability and fluxionality throughout the composition range.**

**Force field model for nucleobase clusters**

**In order to study the non-covalent interactions in nucleobase clusters, we have used force fields such as AMOEBA and OPLSAA.**

**Global optimizations and structural evolution in molecules and clusters**

Faculty Profile